Genome-Wide Data-Mining of Candidate Human Splice Translational Efficiency Polymorphisms (STEPs) and an Online Database

Variation in pre-mRNA splicing is common and in some cases caused by genetic variants in intronic splicing motifs. Recent studies into the insulin gene (INS) discovered a polymorphism in a 5′ non-coding intron that influences the likelihood of intron retention in the final mRNA, extending the 5′ untranslated region and maintaining protein quality. Retention was also associated with increased insulin levels, suggesting that such variants - splice translational efficiency polymorphisms (STEPs) - may relate to disease phenotypes through differential protein expression. We set out to explore the prevalence of STEPs in the human genome and validate this new category of protein quantitative trait loci (pQTL) using publicly available data.